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We all know what emotions are and how they can influence someone’s decisions. In many fields, emotions play a big role and almost every time they can be seen on someone’s face. It is difficult to read the emotions on somebody’s face when you actually concentrate more on what the person is saying. Not everyone can be a multi-tasking master and pay attention to the body language as well (which by the way, sometimes shows the true feelings of a person). In marketing for example, we can see very clearly the attitude that a customer has over a product just by looking at his face. Let us all agree, life would be way easier if we would be always aware of what a person is truly feeling.

Taking in account all that, researchers Hakan Boz and Utku Kose conducted a study and wrote an article called ’’Emotion Extractions from Facial Expressions by Using Artificial Intelligence Techniques’’. Nowadays, in an era where technology becomes more and more dominant in each and every field, these researchers developed a system that is actually able to identify the emotion of a person based on his facial expression. In detail, the system considered a Cascade Feedforward Artificial Neural Network model trained by a recent optimization algorithm called as Vortex Optimization Algorithm.

The main objective is generally finding the points determining different parts of a face. After determining the related parts of a face, it becomes easier to have idea about i.e. facial expression. On the other hand, it is also important that different genders, different races and even changing sizes of a head – face can affect the way of detecting facial expression. So, it has always been important, especially for researchers working on detecting facial expressions by intelligent systems, to use appropriate types of data. The researchers Boz and Kose applied 3 sets of data, the first set containing 490 photos with genders, and adjusted for 7 emotions, the second set including 810 photos with genders, races, and adjusted for 7 emotions and the third set (the authors’ choices) 100 photos chosen randomly and adjusted for 7 emotions.

Different data sets for the application of emotion extraction from facial expressions

In order to train the system, the researchers used Microsoft Kinect 3D infrastructure to gather 3D face points. Microsoft Kinect 3D is a popular system of both hardware and software solutions for effective recognition processes from photos or videos. The results had been positive and the system was able to extract the emotions from different photographs. They used the Vortex Optimization Algorithm. This is an intelligent, Swarm Intelligence based algorithm, which was developed by Kose and Arslan by inspiring from dynamics of vortices in the nature. In detail, the algorithm uses some role-based and evolutionary mechanisms to achieve the optimization solution processes.

In conclusion, the researchers succeeded in developing a system that is effective enough to extract different emotions from photos related to individuals with different gender and even different race. We are all sure that this is the future of technology and soon enough, emotion reading won’t be a secret for the humankind anymore.

Ecaterina Filimon

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Technology has become more than a phenomenon used for innovation, it is now a science put in the service of making human life easier. If in the past, taking a picture of yourself required a voluminous camera with an obscure chamber, nowadays we have our cameras to ourselves for they are incorporated in our gadgets. Therefore, we take pictures of everything we see – recording reality is just one swipe away and ready in the blink of an eye. However, sometimes is difficult to skim through thousands of photos in order to find a specific one. Nabil M. Hewahi and…

The article written by Ahmet Demir from Harahalli Vocational School, Usak University, Ușak, Turkey and Utku Kose from Computer Sciences Application and Research Center, Usak University, Ușak, Turkey is a research which takes into account the importance of optimization and of solving its problems. They propose intelligent optimization techniques based on Artificial Intelligence in order to use them for optimization problems. So that they can provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions, two optimization algorithms are proposed: Vortex Optimization Algorithm (VOA) and Cognitive Development Optimization Algorithm (CoDOA). Optimization is defined as choosing…

We are all familiar with the movies in which robots take control over humans and then chaos is brought into the world just because these forms of artificial intelligence became too developed. But is this actually possible or is it just an invention of the movie directors in order to attract more audience? Researcher Utku Kose tried to depict the possible scenarios in which this could happen and he also came up with some solutions in the article Are We Safe Enough in the Future of Artificial Intelligence? A Discussion on Machine Ethics and Artificial Intelligence Safety. Starting from…

One of the biggest problems that we can encounter when scaling a picture that’s small to a big dimension is the fact that small scaled image are losing some important bits of information which cannot be recovered when scaled back. This problem though, can be fixed using multi-objective genetic algorithm. Researchers Kishor Datta Gupta and Sajib Sen are describing a genetic algorithm approach to recover lost bits while image resized to the smaller version using the original image data bit counts which are stored while the image is scaled. This method is very scalable to apply in a distributed system.…

In order to avoid car accidents and ensure safer roads, Autonomous vehicles (AVs) have been created. These vehicles are capable of sensing its environment and navigating without human input, which is indeed a great step towards more secure experiences for drivers. As a method of improvement for the AVs, agent-based collision avoidance components that represent human cognition and emotions have been designed. However, agent-based solutions have not been validated using any key validation technique.

Considering this lack of validation practices, the authors F. Riaz and M. A. Niazi present in their upcoming paper state-of-the-art Emotion Enabled Cognitive Agent (EEC_Agent), which was proposed to avoid lateral collisions between semi-AVs. It is stated in the paper that the main drawback of EEC_Agent is that it is claiming utilization of emotions in making collision avoidance decisions, but no proper emotion generation mechanism has been proposed, which helps the cognitive agent to feel emotion according to the changing in the dynamic environment. In order to overcome this problem, they have redesigned the architecture of EEC_Agent using EABM (Exploratory Agent Based Modeling) and explored the role of OCC (Ortony, Clore & Collins) model in the fear generation of EEC_Agent.

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